Embed Size (px)
Citation preview
May 7 – 9, 2019
Data Governance Considerations with S/4 Andrew Evans/Amar Reddy ‐ PwC
Session # 82318
About the Speakers
Andrew Evans
Managing Director, PwCAndy has over eighteen years of experience in data transformation engagements. Andy’s focus over the last ten years has focused on data migration, master data management, and data governance solutions for large organizations.
Amar Reddy
Director, PwCAmar is a Director focusing on data transformation, data quality, data remediation, MDM and Master data governance. Amar has over 18 years SAP/DATA experience, including 15+ years assisting clients in full life cycle Implementation of enterprise system solutions and large enterprise transformations. Areas of expertise includes SAP ECC, SAP S/4, SAP MDG.
Key Outcomes/Objectives
1.Understand the Importance of Data Governance 2.Different components of Data Governance3. SAP tool capabilities to enable Data Governance
Agenda
• Challenges and benefits of data governance• Key data governance components• Effective implementation approach• SAP MDG Capabilities • Q&A
BUSINESS CHALLENGES AND BENEFITS OF DATA GOVERNANCE
Data Governance Considerations – S/4 Business Transformation
As part of typical S/4 business transformation many organizations do not make data governance a priority, mainly due to lack of knowledge of business implications resulting from improper governance of data. Although the S/4 implementation could be successful without data governance, addressing master data governance as an afterthought likely will lead to significant challenges, roadblocks and risk.
Absence of master data governance can result in: • Incorrect business decisions due to poor quality of data• Delayed revenue recognition and cash bookings, Loss of customers• High impact on business efficiency & productivity due to lack of accurate, complete, consistent data • Non‐compliance from lack of enforcement of SOD, Security controls, Roles and Responsibilities• High total cost of ownership per data record across data dimensions• Unstructured processes, inconsistent methods from region to region and function to function• Lack of ownership and accountability• Inadequate measuring and monitoring of quality metrics. Non‐automated checks
Business Challenges
The challenges businesses face due to the absence of an effective Master Data Governance framework can result in numerous costs to a business
Inconsistent Data Management:• Life cycle of the data, by domain, is not understood so
completeness is an issue• Master data is not captured at the source • Lack of enterprise view of data, view from the
silos of the sites, functional operations, divisions, and business units
• There are too many variations and practices in the data and processes by division
Application Landscape:• Too many systems can create master data
• Significant amount of data management happens on user’s desktops or laptop devices
Data Management Technology:• The enterprise is missing some of the basic
leading practice data management functionality
• 70% of Data management is data, process, and people and only 30% enabling technology
• Lack of enterprise data management technology to automate governance rules to prevent creation or maintenaning bad data in the future
Governance::• Partial governance has been implemented through
manual methods• Lack of governance and data ownership at an enterprise
level • Data management issues include process
fragmentation, inconsistency, lack of standards, too many users maintaining data
Commitmentfor BusinessOwnership
Business & functional process
knowledge
Scale Governance for your success
Focus on sustaining
value
Data Standards,
Relationships, &Hierarchies
Benefits of Data Governance
• Deliver strategic business objectives
• Increased bottom line growth
• Increased productivity• Stakeholder/customer
satisfaction
• Avoids redundancy Through single version of truth
• Reduced time to reconciliation, faster entry of product and accounting data
• Reduced errors and costs via automated validations and rules
• Enforced security, tracking, notification. SOX compliance and compliance audit trail for adherence
• Accountability and ownership to endorse
• Data privacy, data security, data
Increased Value Enablement
Increase Operational Excellence
Decrease Cost Decrease Risk and Increased Compliance
Increase Business and IT Alignment
• Reduce head count via automation, standardization
• Reduced time in production, order fulfilment and error reconciliation
• Reduce technology costs
• Reduce resources, redundancy
• Reduce systems and technology complexity
• Streamline and consolidate operational support/services
Governance Transformation
‘Data Management’ centric and not ‘Data Governance’ inclined
Limited focus on data stewardship anddata standardization
Data entry process is manual and not standardized.
‘Reactive’ data quality measures and not ‘Proactive’
Instituting data governance organization formalizing various
roles and responsibilities
Emphasis on data stewardship and standardization with role ‘Data Steward’
Establish automated data quality measures and practices with ‘Data
Quality Analyst’
Executing efficient data entry processes using standardized mechanisms and templates with ‘Data Maintainer’
Data Collection &
Transformation
Data Management
Data Governance
Data Quality
Data GovernancePeople + Process + Technology = Quality + Accuracy + Completeness
KEY ELEMENTS OF DATA GOVERNANCE
What is Master Data Governance?
Master data governance refers to the overall management of the availability, usability, integrity, and security of the data in an enterprise. A sound master data governance program includes a governing council, a defined set of procedures, and a plan to execute those procedures by enabling technology
Orchestration of people, processes, and technology to manage the enterprise critical master data assets by using roles, responsibilities, policies, and procedures to ensure that data is accurate, consistent, secure, and aligns with overall enterprise objectives.“ Strategy
Organizational Objectives
Policies, Processes & Standards
Reporting & Monitoring
Distribution & Communication
Comprehensive Master Data Governance defines:
• What is being governed: Products, Vendors, Customers, Cost Centers, Work Centers, etc.
• Who is governing: The roles with decision making rights and responsibilities.
• How is it governed: Processes by which guiding principles, policies, and procedures are established, prioritized, deployed, managed, amended, and enforced.
• When is it being governed: History of managing the object including the creation, updating, deletion, and who made those updates.
Data Governance Framework
Data Governance is the development and FORMAL enforcement of standards, policies, and processes to assign clear accountability forenterprise data assets.
Key Components Needed to Establish Data Governance
Technology and ArchitectureThis component focuses on the How Governance is enabled via Technology (e.g. Data Quality Tools, Reference & Master Data Management Tools, and Metadata Management Tools)
Data Governance
Rules of EngagementThis component focuses on the Why and What of
Governance (e.g. Data Accountabilities, Data Controls, Data Standardization, and Data Quality Metrics).
Policies and ProceduresThis component focuses on the How and When of
Information Governance (e.g. Access management, Privacy, Security, Data Management, Data Retention
People and Organizational BodiesThis component focuses on the Who of Governance (e.g. Data Governance Council, Data Stewardship Council, Business Process & IT Stewardship Councils)
Key Elements of Data Governance – Rules of Engagement
The rules of engagement of an data governance program define the Why and What of the program
The components that deal with Rules & Rules of Engagement are:1. Mission & vision of the program: A mission statement and a clear vision for the data
governance program needs to be developed and socialized.2. Goals, success measures, funding strategies: SMART (Specific, Measurable, Actionable,
Relevant, & Timely) Goals and metrics need to be developed to measure the progress of the governance program.
3. Data Standards, Rules & Definitions: Data related policies, standards, compliance requirements, and definitions need to be developed.
4. Decision Rights: The decision making framework needs to be defined – who gets to make the decision, when, and using what process?
5. Accountabilities: For activities that cross into responsibilities of multiple departments, information governance programs may be expected to define accountabilities that can be incorporated into organization processes.
6. Controls: Controls related to access to information, change management, policies, training, information retention need to be developed.
Decision Making Framework
Data Standard Guidance Document
DG Operations RACI
Key Elements of Data Governance – People and Organizational Bodies
The people & organizational bodies element defines the Who of the data governance program
A data governance program will need to account for the governance requirements of various business functions and potentially define an organization body (e.g. Data stakeholders, Data Stewardship Council, and Data Governance Office).
Samples Roles1. Data Stakeholders: Data stakeholders exist across the organization. These include groups that
create data, those who consume data & information, and those who set rules and requirements for data & information. As each of these stakeholders affect and are affected by data related decisions, their expectations must be addressed by the data governance program
2. Data Governance Office (DGO): The DGO facilitates and supports governance related activities like collecting metrics & measures and reporting on them to stakeholders, providing ongoing stakeholder care in the forms of communication, access to information, record keeping, and education/support
3. Data Stewardship Council: The stewardship council consists of stakeholders who come together to make data related decisions. They may set policies and specify standards, and craft recommendations that are acted on by higher level governance board
Data Governance Operating Model
Roles & Responsibilities
Key Elements of Data Governance – Process and Polices
The processes and policies element defines the How and When of the data governance program
Governance Process Flow
Governance Policy Document
The process and policies element focuses on the methods used to govern data. Ideally, such processes and policies are standardized, well documented, and repeatable.
Example Processes:• Data Classification• Data Standards Creation and Modification• Data Creation, Modification, and Deletion• Issue Resolution• Change Management and CommunicationsExample Policies:• Access Management• Data Security
Key Elements of Data Governance – Technology and Architecture
The technology element focuses on the tools and technologies used to govern data. The architecture element focuses on how the tools will be integrated into existing ecosystem.
Master Data Management
Metadata Management
Reference & Master Data Management
Data Quality Management
Metadata Management
• Define, maintain & enforce Business Rules around Reference & Master Data
• Reconcile reference and master data across disparate systems• Implement Master Data security access rules
• Correlate Data Errors with measurable business impacts• Prioritize & Remediate Data Issues• Monitor performance with respect to data policy compliance
• Capture and Maintain Metadata• Notify stakeholders when metadata changes• Allow consumers to select data for agile consumption
EFFECTIVE IMPLEMENTATION APPROACH
Approach starts with what you are governing and evolves into how will you govern it
Key inputs• Current state standards, policies,
people, process, and systems knowledge
• Industry leading practice
Key inputs• Initial hypothesis of
required capabilities • Gap analysis from interviews• Validation of target capabilities• Change management considerations• Interaction model
Key inputs• Organization model • BU and regional level governance
across enterprise functions• Communications and training
requirements
Determine a governance framework informed by understanding of specific requirements…
resulting in specific governance dimensions, process, and capabilities…
which can them be operationalized across the enterprise, BUs, and regions… and consistently
delivered.
What you need? How you will implement?How you will operate?
Framework for Global Data Management must address a variety of factors in order to be successful
Terms of Reference
Describes the purpose, authority and remit of the DG Steering Group and the DG Working group. The Terms of Reference sets out the responsibilities, operation, and attendees of these groups.
Data GovernanceSteering Group
The steering group manages the running of the data governance organization, provides direction to data users, and sets the agenda for the organization’s Data Strategy.
Organizational Model
An adaptable model is required to align the DG organization to a number of on‐going programs across the organization
Principles & Policies
The principles guide the policies and decision making of the DG Steering and Working Groups, as well as providing high‐level focus for anyone using data within the organization.
Standards
The standards define common data entities, their attributes and inter‐relationships as well as records of authority for each data object and data element.
Processes
The processes show the steps which need to be taken to achieve a range of every day interactions with data, range from reacting to an incident and fixing it to completing compliance returns.
Culture
The culture of the organization needs to drive the culture of change and accountability.
Ownership
A number of key roles are required within the organization to properly execute data governance
Stakeholder Management
Stakeholders need clear and regular communications to reinforce the data governance message and keep them engaged
Reporting & KPIs
Reporting & KPIs are a key element of DG. The steering group should receive regular progress and status reports.
Performance Management
Data governance principles should form part of annual objectives from teams through to individuals at all levels of the business.
Training Materials
Training materials provide an overview of data governance and related issues to staff, covering introductory topics and the more advanced, and including role ‘cheat sheets’.
Training Materials
Training materials provide an overview of data governance and related issues to staff, covering introductory topics and the more advanced, and including role ‘cheat sheets’.
Training Materials
Training materials provide an overview of data governance and related issues to staff, covering introductory topics and the more advanced, and including role ‘cheat sheets’.
1 2 3 4
5
6
7
8
9
10
11
12
Terms of Reference
Data Governance Steering Group
Organizational Model
Principles& Policies
TrainingMaterials
Framework
Roadmap
14
13
MDG Implementation ApproachApproach to Implementing SAP MDG Solution includes five (5) distinct stages:
Assess, Design, Construct, Validate &Deploy and Operate & Review.
Strategy
People
Process
Technology
Data
Delivering Ch
ange
Assess
Understand the current state of the data governance processes, methods.
Identify existing governance organization, roles and responsibilities, ownership and accountability.
Identify business requirements to enable governance via tool such as SAP MDG
Design
Blueprint a scalable solution for a specific domain before proceeding to additional domains in alignment with the data governance Roadmap.
Design Process flows including governance roles, security, business rules. Design MDM processes and data replications to downstream systems. Document Functional specifications
Construct
Utilize agile methodology to build objects in sprints and string test to provide business owners to validate the solution.
Build Workflows, Enhancements and Interfaces to enable the MDM and Governance Solution. Build Security roles and automated notifications.
Document technical specifications and perform unit testing.
Validate & Deploy
Assist in Validating solution through integration testing, regression testing. and User acceptance tests. Defect identification and resolution
Build deployment strategy to include cutover tasks, go live plan, production system landscape strategy
Enable security authorizations and user assignment.
Operate & Review
Develop and support firefighting roles, define and support defect resolution plan, and business continuity plan during hyper care.
Support MDG operations and organization. Actively assist in defect resolution. Define, measure and monitor SLAs, metrics implement continuous improvement. Execute plan to handoff to client..
Key Learnings
When considering developing a data governance framework within your organization, it’s important to consider some key points:
Organization Strategy
• Align your governance approach with your overall data strategy
• Consider a pilot data object to establish new controls around your data
• Perform a strategy assessment including the state of data quality across key data domains to be able to prioritize activities
• Business ownership with IT support• Executive sponsorship is key• Involvement of the right stakeholders at the local and global level
• Include change management/training as part of operationalization
• Have the business part of the process design sessions• Remember process design for the non‐record updates
Process Technology
• Review data architecture in order to optimize data governance
• Focus on establishing standards, policies and procedures and an organization structure before building out the technology
SAP MDG CAPABILITIES
SAP MDG Capabilities
Consolidation and Centralizationof Master Data
Governance Workflows
Data Qualityand Validation
Checks
Master Data Hierarchies
Standard Master Data Replications to SAP & Non‐SAP
Systems
Mass Processing
• With one source of truth users have the ability to trust their data• Users don’t need consistently to navigate to multiple systems to manually
reconcile data• Improves reporting accuracy and increases transparency
• Reduces a number of manual checks that users need to perform on a daily basis
• Improves trust with automated controls • Reduces the possibility of duplicate master data
• Standard hierarchies are user friendly and available for setup with master data objects
• With hierarchies, reporting benefits can increase where “drill‐down” functions are more accessible and executives can make better business decisions
• Workflows can be configured based off standard configuration tables instead of coding development
• Workflows enforce automated controls by ensuring approvals and can reduce time spent on gathering documentation for audits
• Workflows improve operational efficiency by reducing the need for manual approvals (i.e. through email)
• Standard data filters can be applied to outbound data replications• Reduces implementation costs with..
• Given the ability to upload and update master data can reduce time spent on repetitive activities
• Templates can be created for mass processing activities
• Business activities such as acquisitions often involve the need to mass upload master data to support transactions
SAP MDG
SAP MDG Standard out of the box Data Models
A data model in Master Data Governance is comprised of various elements (entity types, attributes, and relationships) to enable model master data structures of any complexity in the system. These elements are described below
Data Centralization/Consolidation
• Comprehensive view of all master data objects
• Up‐to‐date consistent information that’s reflected across all connected systems
• Reliable data helps increase reporting accuracy
• Reduced time spent on reconciling master data discrepancies
Consolidation of master data is key to provide a single source‐of‐truth
Governance WorkflowsAdaptable ‐Workflow is adaptable based on business requirements. Most clients have taken advantage of the of the role based and rule based workflows as adapts to any business requirement whether it standard or complex approval procedures. Linear or Distributed ‐Workflow can be configured as linear or distributed as the clients requirements. Hence, majorly the clients do you use both which fits the business requirement.Distributed Workflow – Commonly used when the business requirement is complex in nature, which helps the business to fit any kind of process multiple levels of approvals, by using the MDG BRF (Business rule framework) clients have been able to achieve flawless results.
Data Staging ‐ Data that is in process will reside in a separate staging area and will not be replicated to operational databases until after final approval.Business Rules ‐ Business Rules can be embedded within processes. (i.e. if a supplier’s bank data is changed, the request should route to a treasury specialist before final approval this can be achieved my harmonizing the present business process with MDG’s out‐of‐box Business Rule framework (BRF))
Data Replication Framework• Filters can be applied to the replication where only specified data is sent to each system• Key mappings can be leveraged to map object IDs between systems, if IDs differ (i.e. business is upgrading to S/4
HANA and has new vendor IDs in S/4 than used in legacy systems – mapping of legacy IDs to new S/4 IDs can be maintained in MDG)
• Value mappings of different fields between systems can be transformed through MDG (i.e. payment method value “T” in system A = value “C” in system B)
• Standard replication monitoring capabilities to support error handling • Multiple replication methods can be used (RFC, ALE, SOA) based off capabilities of connected systems
Take the Session Survey.
We want to hear from you! Be sure to complete the session evaluation on the SAPPHIRE NOW and ASUG Annual Conference mobile app.
Access the slides from 2019 ASUG Annual Conference here: http://info.asug.com/2019‐ac‐slides
Presentation Materials
©2019 PwC. All rights reserved. PwC refers to the US member firm or one of its subsidiaries or affiliates, and may sometimes refer to the PwC network. Each member firm is a separate legal entity. Please see www.pwc.com/structure for further details.
This content is for general information purposes only, and should not be used as a substitute for consultation with professional advisors.
Thank You
Let’s Be Social.Stay connected. Share your SAP experiences anytime, anywhere.
Join the ASUG conversation on social media: @ASUG365 #ASUG
